An information processing apparatus includes a processor. The processor is configured to estimate whether a fellow passenger is included in occupants of a vehicle, when the fellow passenger is included in the occupants, acquire information relating to a driver of the vehicle and information relating to the fellow passenger, estimate a relationship between the driver and the fellow passenger based on the acquired information, and select a control relating to traveling of the vehicle according to the relationship.

A method of using a control system to estimate range of an electrified vehicle operated by a driver includes monitoring a first set of driver behaviors while the vehicle is in operation and comparing the monitored first set of driver behaviors to a plurality of known profiles having respective stored behaviors. The method may include matching the first set of driver behaviors to at least one of the known profiles to create an adapted driver model, modeling an adapted drive cycle profile based on the matched adapted driver model, and calculating a predicted driving range based on the adapted drive cycle profile. The method may classify the monitored first set of driver behaviors as at least one of conservative, neutral, and aggressive, relative to the plurality of known profiles, and model the adapted drive cycle profile is further based on the conservative, neutral, or aggressive classification.

Methods and systems are provided for cloud processing data streamed from a vehicle and a home (e.g., any location) associated with a user account. One method includes receiving a data stream from the vehicle entity, where the data stream from the vehicle entity includes metadata from one or more data producing objects of the vehicle entity. And, receiving a data stream from the home entity, where the data stream from the home entity includes metadata from one or more data producing objects of the home entity. The method includes accessing action conditions associated with a user account. The action conditions identify a position where at least one or more states of the metadata from each of the home entity and the vehicle entity intersect. And, each action condition identifies a type or types of control information to be processed. The method includes processing the received metadata from the vehicle entity and the home entity. The processing identifies metadata of the home entity and the vehicle entity that includes an intersection of said at least one or more states of said respective metadata of the home entity and the vehicle entity. The intersection is indicative that a specific action condition being satisfied. The method includes sending, in response to the specific action condition being satisfied, control information to the user account. The logic associated with the user account determines when the control information is sent to the vehicle entity or the home entity for surfacing information or making a setting regarding the satisfied specific action condition. Intersections can also be identified with user devices that may be associated with the user account.

An optimization system includes a processor configured to receive one or more criteria for optimizing use of a power storage device in an application device, to receive data comprising at least one of user input(s), data from a cloud, and data regarding the application device, and to determine a charging/discharging profile for charging or discharging the power storage device from the received data based on the one or more criteria. The optimization system further includes an output configured to output the charging/discharging profile. The processor is configured to receive user feedback regarding the charging/discharging profile, machine learning data, big data, and/or a change regarding the optimization system and/or the application device, and to update the charging/discharging profile based on the received at least one of the user feedback regarding the charging/discharging profile, machine learning data, big data and the change regarding the optimization system and/or the application device.

Autonomous vehicle fleet management systems are provided herein. An example method includes receiving, via a control module of a first electric vehicle, trip characteristics data associated with a second electric vehicle. The trip characteristics data includes information such as vehicle location, a trip destination, and a route plan associated with the second electric vehicle. The control module or a connected control server selects a charging station for recharging the first electric vehicle based at least in part on the trip characteristics data and at least one route optimization option associated with the first electric vehicle. The example method further includes determining a travel route to the charging station and navigating the first electric vehicle to the charging station along the travel route using an autonomous vehicle navigation system associated with the control module.

An information processing device includes a control unit configured to determine whether to include a regenerative brake in specifications of a proposed vehicle based on regenerative brake operation data acquired while a first vehicle including the regenerative brake is traveling in a predetermined area. The proposed vehicle is a vehicle proposed as a vehicle for a customer to drive in the predetermined area.

A hybrid leisure vehicle equipped with an electric motor and an engine includes: an accelerator sensor that detects an operation amount of an accelerator operation member operated by a user; a controller that includes a processor and a memory and that controls the electric motor and the engine based on a detection signal of the accelerator sensor; and a selection input device connected to the controller and operated by the user. The memory stores a plurality of mode programs each of which specifies how to change states of the electric motor and the engine depending on vehicle-related parameters. The processor selects a mode program from the plurality of mode programs in response to an input provided by the user to the selection input device, and controls the electric motor and the engine according to the selected mode program.

The present disclosure describes a system for determining an optimal gear ratio for a light electric vehicle. The optimal gear ratio may be based on an anticipated or current route of an individual riding the light electric vehicle, riding habits of the individual and/or on maintenance status information associated with the light electric vehicle. When the optimal gear ratio is determined, the system provides an indication of the optimal gear ratio to the light electric vehicle.

The present disclosure relates to a dual battery system for in a dual manner powering propulsion of an electric vehicle including a first electric motor coupled in driving relationship to one or more rear wheels and a second electric motor coupled in driving relationship to one or more front wheels. The dual battery system includes a first battery and a second battery. The first battery is configured to provide electric power for driving the first electric motor and the second battery is configured to provide electric power for driving the second electric motor. The present disclosure also relates to an electric vehicle including such a dual battery system.

Systems and methods are provided for determining an optimal point during operation of a hybrid electric vehicle towing a trailer at which the energy that can be recouped through regenerative braking is maximized. Operating conditions or characteristics of the hybrid electric vehicle and the brake characteristics of the trailer are considered in determining the optimal point at which the energy that can be recouped is maximized. At the optimal point at which the energy that can be recouped is maximized, the engine of the hybrid electric vehicle is turned off. Otherwise, the engine of the hybrid electric vehicle is left on.